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<title>Qualitative Modeling & Diagnosis</title>

<h1>Qualitative Modeling & Diagnosis</h1>

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<b><li> Qualitative Multiple-Fault Diagnosis of Continuous Dynamic Systems Using
Behavioral Modes<br></b>
Siddarth Subramanian and Raymond J. Mooney<br>

<cite>Proceedings of the Thirteenth National Conference on Aritificial Intelligence</cite>,
pp. 965-970, Portland, OR, August, 1996. (AAAI-96)<p>

<blockquote>
Most model-based diagnosis systems, such as GDE and Sherlock, have
concerned discrete, static systems such as logic circuits and use
simple constraint propagation to detect inconsistencies. However,
sophisticated systems such as QSIM and QPE have been developed for
qualitative modeling and simulation of continuous dynamic systems.  We
present an integration of these two lines of research as implemented
in a system called QDOCS for multiple-fault diagnosis of continuous
dynamic systems using QSIM models. The main contributions of the
algorithm include a method for propagating dependencies while solving
a general constraint satisfaction problem and a method for verifying
the consistency of a behavior with a model across time. Through
systematic experiments on two realistic engineering systems, we
demonstrate that QDOCS demonstrates the best balance of generality,
accuracy, and efficiency among competing methods.
</blockquote>

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<b><li>Qualitative Multiple-Fault Diagnosis of Continuous Dynamic Systems Using
Behavioral Modes<br></b>

Siddarth Subramanian<br>

Ph.D. Thesis, Department of Computer Sciences, University of Texas at Austin, August, 1995.<p>

<blockquote>

As systems like chemical plants, power plants, and automobiles get
more complex, online diagnostic systems are becomingly increasingly
important.  One of the ways to rein in the complexity of describing
and reasoning about large systems such as these is to describe them
using qualitative rather than quantitative models.<p>

Model-based diagnosis is a class of diagnostic techniques that use
direct knowledge about how a system functions instead of expert rules
detailing causes for every possible set of symptons of a broken
system.  Our research builds on standard methods for model-based
diagnosis and extends them to the domain of complex dynamic systems
described using qualitative models.<p>

We motivate and describe out algorithm for diagnosing faults in a
dynamic system given a qualitative model and a sequence of qualitative
states.  The main contributions in this algorithm include a method for
propagating dependencies while solving a general constraint
satisfaction problem, and a method for verfying the compatibility of a
behavior with a model across time.  The algorithm can diagnose
multiple faults and uses models of faulty behavior, or behavioral
modes.<p>

We then demonstrate these techniques using an implemented program
called QDOCS and test it on some realistic problems.  Through our
experiments with a model of the reaction control system (RCS) of the
space shuttle and with a level-controller for a reaction tank, we show
that QDOCS demonstrates the best balance of generality, accuracy and
efficiency among known systems.<p>
</blockquote>

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<b> <li> Multiple-Fault Diagnosis Using General Qualitative Models with Fault Modes <br> </b>  

Siddarth Subramanian and Raymond J. Mooney <br>

<cite>Working Notes of the IJCAI-95 Workshop on Engneering Problems for Qualitative Reasoning</cite>, Monreal, Quebec, August 1995.<p>

<blockquote>
This paper describes an approach to diagnosis of systems described by
qualitative differential equations represented as QSIM models.  An
implemented system QDOCS is described that performs multiple-fault,
fault-model based diagnosis, using constraint satisfaction techniques,
of qualitative behaviors of systems described by such models. We
demonstrate the utility of this system by accurately diagnosing
randomly generated faults using simulated behaviors of a portion of
the Reaction Control System of the space shuttle.
</blockquote>

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<b><li>Multiple-Fault Diagnosis Using General Qualitative Models with Fault Modes<br></b>  
Siddarth Subramanian and Raymond J. Mooney <br>

<cite> Working Papers of the Fifth International Workshop on
Principles of Diagnosis</cite>, pp. 321-325, New Paltz, NY, 1994. <p>

<blockquote>
This paper describes an approach to diagnosis of systems described by
qualitative differential equations represented as QSIM models.  An
implemented system QDOCS is described that performs multiple-fault,
fault-model based diagnosis, using constraint satisfaction techniques,
of qualitative behaviors of systems described by such models. We
demonstrate the utility of this system by accurately diagnosing
randomly generated faults using simulated behaviors of a portion of
the Reaction Control System of the space shuttle.
</blockquote>

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<b> <li>Learning Qualitative Models for Systems with Multiple Operating Regions<br></b>

Sowmya Ramachandran, Raymond J. Mooney and Benjamin J. Kuipers <br>

<cite>Proceedings of the Eight International Workshop of Qualitative
Reasoning about Physical Systems</cite>, pp. 212-223, Nara, Japan,
June 1994. (QR-94)

<blockquote>
The problem of learning qualitative models of physical systems from
observations of its behaviour has been addressed by several
researchers in recent years. Most current techniques limit themselves
to learning a single qualitative differential equation to model the
entire system.  However, many systems have several qualitative
differential equations underlying them.  In this paper, we present an
approach to learning the models for such systems.  Our technique
divides the behaviours into segments, each of which can be explained
by a single qualitative differential equation.  The qualitative model
for each segment can be generated using any of the existing techniques
for learning a single model.  We show that results of applying our
technique to several examples and demonstrate that it is effective.
</blockquote>

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<b> <li> Automatic Abduction of Qualitative Models </b> <br> 

Bradley L. Richards, Ina Kraan, and Benjamin J. Kuipers <br> 

<cite> Proceedings of the Tenth National Conference on Artificial
Intelligence</cite>, pp. 723-728, San Jose, CA, July 1992. <p>

<blockquote>
We describe a method of automatically abducing qualitative models from
descriptions of behaviors.  We generate, from either quantitative or
qualitative data, models in the form of qualitative differential equations
suitable for use by QSIM.  Constraints are generated and filtered both by
comparison with the input behaviors and by dimensional analysis.  If the
user provides complete information on the input behaviors and the
dimensions of the input variables, the resulting model is unique,
maximally constrainted, and guaranteed to reproduce the input behaviors.
If the user provides incomplete information, our method will still
generate a model which reproduces the input behaviors, but the model
may no longer be unique.  Incompleteness can take several forms:  missing
dimensions, values of variables, or entire variables.
</blockquote>

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<address><!WA12><a href="http://www.cs.utexas.edu/users/estlin/">estlin@cs.utexas.edu</a></address>




